期刊
JOURNAL OF MATERIALS CHEMISTRY C
卷 10, 期 9, 页码 3404-3417出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1tc03776e
关键词
-
资金
- DAE, Government of India
Machine learning models are used to filter and select stable magnetic materials with large magnetization and magnetic anisotropy energy. This method reduces computational cost significantly and has practical applications in screening candidate materials and designing permanent magnets.
Machine learning models are developed to hierarchically filter and select stable magnetic materials with large magnetization and magnetic anisotropy energy. Starting from an initial set of 278 materials, 10 are identified by the models to satisfy the desired target properties. Subsequent first principles calculations find 7 stable compounds with large moment and high anisotropy energy. This machine learning assisted filtering procedure reduces computational cost by more than an order of magnitude. Over an expanded search space, it is expected to lead to even more dramatic reduction in computational time. This method can find practical use in screening candidate materials for 2D magnets with high magnetization, and also in design of permanent magnets without rare earth elements.
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